Proceedings ArticleDOI
Towards Designing an Adaptive Framework for Facial Image Quality Estimation at Edge
Aditya Deshpande,Alisha Shahane,Darshana Gadre,Mrunmayi Deshpande,Bhushan Garware,Siddhivinayak Kulkarni +5 more
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TLDR
This paper uses machine learning algorithms to map the relationship between image quality features and performance of facial recognition, and uses deep learning to build a binary classifier which accepts or rejects images before sending them for actual facial recognition.Abstract:
This paper proposes a framework for facial image quality estimation in order to address the limitation of real-time applicability of facial recognition. This framework determines whether an image is suitable for facial recognition. We first exploit machine learning algorithms to map the relationship between image quality features and performance of facial recognition. We extract a variety of features (like focus measure, brightness, obscured face) and study their influence on the accuracy of face recognition. After examining the results of this approach, we then used deep learning to build a binary classifier which accepts or rejects images before sending them for actual facial recognition. This decision is taken based on the probability of the facial recognition framework correctly matching a face from the image. We used images from the Chokepoint dataset, and OpenFace-an open source facial recognition software, for building our framework.read more
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Journal ArticleDOI
Performance of Biometric Quality Measures
Patrick J. Grother,Elham Tabassi +1 more
TL;DR: This work documents methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality, motivated by a need to test claims that quality measures are predictive of matching performance.
Proceedings ArticleDOI
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition
TL;DR: An efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face is proposed.
Journal ArticleDOI
Small-sample precision of ROC-related estimates
Blaise Hanczar,Jianping Hua,Chao Sima,John N. Weinstein,Michael L. Bittner,Edward R. Dougherty +5 more
TL;DR: A simulation study using data models and analysis of real microarray data shows that for small samples the root mean square differences of the estimated and true metrics are considerable, and even for large samples, there is only weak correlation between the true and estimated metrics.
Report on the Evaluation of 2D Still-Image Face Recognition Algorithms
TL;DR: This is the first time NIST has reported accuracy of face identification algorithms, and formally supports use of fast search algorithms such as indexing, partitioning and binning to national-size populations.
Patent
Learning deep face representation
TL;DR: Huang et al. as discussed by the authors proposed a pyramid convolutional neural network (PCNN) for face representation, which adopts a greedy-filter-and-down-sample operation.